Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective
This work addresses the problem of efficient machine learning for devices with limited computational capabilities, which is significant for applications in resource-constrained environments.
The authors tackled the problem of resource-intensive machine learning methods by proposing a maximum-margin hyperdimensional computing classifier, which outperforms baseline methods on several benchmark datasets. The proposed method achieves significant performance gains, although specific numbers are not provided.
Overparameterized machine learning (ML) methods such as neural networks may be prohibitively resource intensive for devices with limited computational capabilities. Hyperdimensional computing (HDC) is an emerging resource efficient and low-complexity ML method that allows hardware efficient implementations of (re-)training and inference procedures. In this paper, we propose a maximum-margin HDC classifier, which significantly outperforms baseline HDC methods on several benchmark datasets. Our method leverages a formal relation between HDC and support vector machines (SVMs) that we established for the first time. Our findings may inspire novel HDC methods with potentially more hardware-oriented implementations compared to SVMs, thus enabling more efficient learning solutions for various intelligent resource-constrained applications.